START

Goal/Purpose of operations: 
The DepMap/PRISM’s primary  pooled drug screens were used to help evaluate if a candidate could be a suitable candidate (if that drug was tested). The primary screen calculated the median of log fold change median fluorescence intensity between replicates of a cell line treated with a drug. The PRISM study considered a cell line as sensitive to a treatment if the median-collapsed fold-change is less than 0.3. 

Finished psedocode on: 
220503

System which operations were done on:
my laptop

GitHub Repo:
Transfer_Learning_R03

Docker:
rstudio_cancer_dr

Directory of operations:
/home - docker

Scripts being edited for operations: 
NA

Data being used: 
PRISM/DEPMAP data downloaded from depmap.org data explore tool. 220503- download data
/output/TF_L_GBM/220503_PRISM_DEPMAP_candidate_data/

Papers and tools:
NA

STEPS

Set working directory

load in data

primary_gbm_res <- read.csv(file= "~/output/220808_prism_gbm_candidates.csv" )
library(readr)
cell_gbm_info<- read_csv("~/data/DEPMAP_PRISM_220228/cell-line-selector_gbm_top_ten.csv")
## Rows: 61 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): lineage3, depmapId, displayName, Tumor Type, Gender, Microsatellit...
## dbl (23): temozolomide Drug sensitivity (PRISM Repurposing Primary Screen) 1...
## lgl  (1): LongTable-checkbox
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

remove the odd names

cell_gbm_info<- cell_gbm_info[, -c(1:3, 5)]
library(tidyr)
cell_gbm_info_longer <- pivot_longer(cell_gbm_info, c(2:12), names_to = "drug", values_to = "log2change")

removing the extra info in the drug stuff

cell_gbm_info_longer$drug <- sub(" .*", "", cell_gbm_info_longer$drug)

remove NAs from the logfold change data

cell_gbm_info_longer_v2 <- cell_gbm_info_longer[ ! is.na(cell_gbm_info_longer$log2change),]

Analysis

library(ggplot2)
library(cowplot)
library(viridis)
## Loading required package: viridisLite
library(circlize)
## ========================================
## circlize version 0.4.15
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
## 
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
##   in R. Bioinformatics 2014.
## 
## This message can be suppressed by:
##   suppressPackageStartupMessages(library(circlize))
## ========================================
library(ComplexHeatmap)
## Loading required package: grid
## ========================================
## ComplexHeatmap version 2.10.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
## 
## If you use it in published research, please cite:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
##   genomic data. Bioinformatics 2016.
## 
## The new InteractiveComplexHeatmap package can directly export static 
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
## 
## This message can be suppressed by:
##   suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================
p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= drug, color= drug))+ geom_point(stat = "identity") +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) 

p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= drug)) + geom_density(alpha=.5) +theme_minimal() +  theme(
    axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)

plot_grid(p1, p2, ncol = 1, align = "v")

cell_gbm_info_longer_v2$tmz_cell<- ifelse(cell_gbm_info_longer_v2$displayName %in% c("A172", "GB1","KNS81", "SF295", "YH13", "YKG1"), "TMZ_RES", "TMZ_Sen")
gbm_candidate_prism_plot_tmz <- function(cell_line_info, drug){
  
  cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug %in% c(drug, "temozolomide"),]
    
  #cell_gbm_info_longer_v2$shape <- ifelse(cell_gbm_info_longer_v2$tmz_cell== "TMZ_RES", 19, 17)
  cell_gbm_info_longer_v2$tmz_cell<- factor(cell_gbm_info_longer_v2$tmz_cell, levels= c("TMZ_Sen", "TMZ_RES" ))
  p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= drug, shape= tmz_cell))+ geom_point(stat = "identity", size = 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) #+ theme(axis.text.y = element_text( color = a))

  p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= drug)) + geom_density(alpha=.5) +theme_minimal() +  theme(
    axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)

  plot_grid(p1+ scale_fill_manual( values=c("#440154FF","#228C8DFF")), p2+ scale_color_manual( values= c("#440154FF","#228C8DFF") ), ncol = 1, align = "v")
}
gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "simvastatin")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "pamidronate")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "floxuridine")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "nimodipine")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "vardenafil")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "moxifloxacin")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "diltiazem")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "diflunisal")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "saxagliptin")

gbm_candidate_prism_plot_tmz(cell_gbm_info_longer_v2, "icosapent")

want to determine if there is a difference between groups and drug repesonse

#mgmt methylation
cell_gbm_info_longer_v2$MGMT_METH<-  ifelse(cell_gbm_info_longer_v2$`MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504` >0.5, "Hyper methylation (>0.5)", "Hypo methylation (=< 0.5)")

drugs<- unique(cell_gbm_info_longer_v2$drug)
mgmt<- c()
for (i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
  #test<- cell_line_info[,c(index[j], 18)]
  cell_line_info<- cell_line_info[!is.na(cell_line_info$MGMT_METH),]
  #groups<- 
  test<- as.vector(as.data.frame(cell_line_info[,18]))
    x<- test[cell_line_info$MGMT_METH == "Hyper methylation (>0.5)",1]
    y<- test[!cell_line_info$MGMT_METH == "Hyper methylation (>0.5)",1]
    x<- as.numeric(unlist(x))
    y<- as.numeric(unlist(y))
    if(length(x) > 1 & length(y) > 1 ){
      res <- wilcox.test(x,y,alternative = "two.sided") 
       mgmt[i]<- res$p.value
    }
}
#egfr COPY NUMBER
cell_gbm_info_longer_v2$EGFR_copy <- ifelse(cell_gbm_info_longer_v2$`EGFR (ERBB1, ERBB) Copy Number 22Q2 Public` >2, "Copy Number > 2", "Copy Number =< 2")

egfr_amp<- c()
for (i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
  #test<- cell_line_info[,c(index[j], 18)]
  cell_line_info<- cell_line_info[!is.na(cell_line_info$EGFR_copy ),]
  #groups<- 
  test<- as.vector(as.data.frame(cell_line_info[,18]))
    x<- test[cell_line_info$EGFR_copy  == "Copy Number > 2",1]
    y<- test[!cell_line_info$EGFR_copy  == "Copy Number > 2",1]
    x<- as.numeric(unlist(x))
    y<- as.numeric(unlist(y))
    if(length(x) > 1 & length(y) > 1 ){
      res <- wilcox.test(x,y,alternative = "two.sided") 
       egfr_amp[i]<- res$p.value
    }else{
      print(i)
    }
    
}
## [1] 1
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## [1] 5
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## [1] 11
#note most of the gbm cell lines have no egfr amplication
#table(cell_gbm_info_longer_v2$Gender)
sex<- c()
for (i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
  #test<- cell_line_info[,c(index[j], 18)]
  cell_line_info<- cell_line_info[!is.na(cell_line_info$Gender ),]
  #groups<- 
  test<- as.vector(as.data.frame(cell_line_info[,18]))
    x<- test[cell_line_info$Gender  == "Male",1]
    y<- test[!cell_line_info$Gender  == "Male",1]
    x<- as.numeric(unlist(x))
    y<- as.numeric(unlist(y))
    if(length(x) > 1 & length(y) > 1 ){
      res <- wilcox.test(x,y,alternative = "two.sided") 
       sex[i]<- res$p.value
    }else{
      print(i)
    }
    
}
#tmz non-senssitive cell lines (0.3=> logfoldchange in PRISM)
#A172
#GB1
#KNS81
#SF295
#YH13
#YKG1

cell_gbm_info_longer_v2$tmz_cell<- ifelse(cell_gbm_info_longer_v2$displayName %in% c("A172", "GB1","KNS81", "SF295", "YH13", "YKG1"), "TMZ_RES", "TMZ_Sen")

tmz<- c()
for (i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
  #test<- cell_line_info[,c(index[j], 18)]
  cell_line_info<- cell_line_info[!is.na(cell_line_info$tmz_cell ),]
  #groups<- 
  test<- as.vector(as.data.frame(cell_line_info[,18]))
    x<- test[cell_line_info$tmz_cell  == "TMZ_RES",1]
    y<- test[!cell_line_info$tmz_cell  == "TMZ_RES",1]
    x<- as.numeric(unlist(x))
    y<- as.numeric(unlist(y))
    if(length(x) > 1 & length(y) > 1 ){
      res <- wilcox.test(x,y,alternative = "two.sided") 
       tmz[i]<- res$p.value
    }else{
      print(i)
    }
    
}
drugs<- unique(cell_gbm_info_longer_v2$drug)
index<- c(3,4,10,11,12)
test_res<- matrix(nrow= 11, ncol= 5)
for( i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
  for(j in 1:5){
    test<- cell_line_info[,c(index[j], 18)]
    groups<- test[,1]== 1
    x<- test[groups,2]
    y<- test[!groups,2]
    x<- as.numeric(unlist(x))
    y<- as.numeric(unlist(y))
    if(length(x) > 1 & length(y) > 1 ){
      res <- wilcox.test(x,y,alternative = "two.sided") 
      test_res[i,j]<- res$p.value
    }else{
      res<- NA
      test_res[i,j]<-res
    }
  }
} 
rownames(test_res)<- drugs
colnames(test_res)<- colnames(cell_gbm_info_longer_v2)[index]
test_res<- cbind(test_res, sex, tmz,  mgmt)
test_res_filter_1<- test_res< 0.05
#p-value for the MGMT promotor
test_res_filter_2<- test_res< 0.16795666
gbm_candidate_prism_plot_two_groups <- function(cell_line_info, drug, info_index, divider= 0, group1="Normal", group2= "Mutation" ){
  
  #index_col <- grepl(colnames(cell_line_info), info)
  cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug == drug,]
  
  a <- ifelse(cell_gbm_info_longer_v2[,info_index] ==divider , 19, 17)
  cell_gbm_info_longer_v2$test <- ifelse(cell_gbm_info_longer_v2[,info_index] ==divider , group1, group2)
  #print(a)
  #nam <- colnames(cell_gbm_info_longer_v2)[info_index]
  #cell_gbm_info_longer_v2[,info_index]<- as.factor(cell_gbm_info_longer_v2[,info_index])
  #print(cell_gbm_info_longer_v2[,info_index])
  p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= test))+ geom_point(stat = "identity", shape = a, size = 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) 
  #+ theme(axis.text.y = element_text( color = a))

  p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, fill= test)) + geom_density(alpha=.5) +theme_minimal() +  theme(
    axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)

  plot_grid(p1+ scale_fill_manual( values=c("#440154FF","#228C8DFF")), p2+ scale_color_manual( values= c("#440154FF","#228C8DFF") ), ncol = 1, align = "v")
  
}
#pten pamidronate
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 12 )

#Sex
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "vardenafil", 14, divider= "Male", group1="Male", group2= "Female")

cell_gbm_info_longer_v2<- cell_gbm_info_longer_v2[,c(1:18, 20,21, 19)]

different p-value threshold because of low poer. DO NOT TRUST THIS DATA

#"EGFR_copy"
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "diflunisal", 21, divider= "TMZ_RES", group1="TMZ Resistant", group2= "TMZ Sensitive")

if they are reistant to tmz it looks reistant here.

gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "icosapent", 21, divider= "TMZ_RES", group1="TMZ Resistant", group2= "TMZ Sensitive")

gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "floxuridine", 19, divider= "Hyper methylation (>0.5)", group1="Hyper methylation (>0.5)", group2= "Hypo methylation (=< 0.5)")
## Warning: Removed 4 rows containing missing values (geom_point).

gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 14, divider= "Male", group1="Male", group2= "Female")

gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "icosapent", 14, divider= "Male", group1="Male", group2= "Female")

#pten 
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 12 )

#"TP53 (LFS1, p53) Mutation 22Q2 Public
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 3 )

#"TP53 (LFS1, p53) Mutation 22Q2 Public
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "floxuridine", 3 )

#"RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public" 
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 10 )

#"RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public" 
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 10 )

#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"   
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "moxifloxacin", 11 )

#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"   
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "pamidronate", 11 )

#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"   
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "temozolomide", 11 )

#"MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"   
gbm_candidate_prism_plot_two_groups(cell_gbm_info_longer_v2, "simvastatin", 11 )

linear regression stuff with methylation of MGMT and efgr amplication

egfr_copy_p<- c()
for (i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
  #test<- cell_line_info[,c(index[j], 18)]
  cell_line_info<- cell_line_info[!is.na(cell_line_info$`EGFR (ERBB1, ERBB) Copy Number 22Q2 Public` ),]
  #groups<- 
  test<- as.data.frame(cell_line_info[,c(5, 18)])
  res <- lm(log2change ~ `EGFR (ERBB1, ERBB) Copy Number 22Q2 Public`, test)
  egfr_copy_p[i]<- summary(res)$coefficients[,4][2] 
  
}
mgmt_methy_p<- c()
for (i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug == drugs[i],]
  #test<- cell_line_info[,c(index[j], 18)]
  cell_line_info<- cell_line_info[!is.na(cell_line_info$`MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504` ),]
  #groups<- 
  test<- as.data.frame(cell_line_info[,c(2, 18)])
  res <- lm(log2change ~ `MGMT Methylation (1kb upstream TSS) MGMT_10_131264504_131265504`, test)
  mgmt_methy_p[i]<- summary(res)$coefficients[,4][2] 
  
}
test_res <- cbind(test_res, egfr_copy_p, mgmt_methy_p)
gbm_candidate_prism_plot_cont <- function(cell_line_info, drug, info_index, title ){
  
  #index_col <- grepl(colnames(cell_line_info), info)
  cell_gbm_info_longer_v2<- cell_line_info[cell_line_info$drug == drug,]


  nam <- colnames(cell_gbm_info_longer_v2)[info_index]
  #cell_gbm_info_longer_v2[,info_index]<- as.factor(cell_gbm_info_longer_v2[,info_index])
  #print(cell_gbm_info_longer_v2[,info_index])
  p2 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change, y= displayName, color= get(nam)))+ geom_point(stat = "identity", size= 4) +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3)+ labs(color=title) 
  #+ theme(axis.text.y = element_text( color = a))

  p1 <- ggplot(cell_gbm_info_longer_v2, aes(x=log2change)) + geom_density(alpha=.5) +theme_minimal() +  theme(
    axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)
  
  legend <- get_legend(p2)
  p2<- p2 + theme(legend.position='none')
  ggdraw(plot_grid(plot_grid(p1, p2, ncol=1, align='v'),
                 plot_grid(NULL, legend, ncol=1),
                 rel_widths=c(1, 0.2)))
  
}
gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "moxifloxacin", 2, "MGMT Promoter Methylation(0-1)" )

gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "floxuridine", 2, "MGMT Promoter Methylation(0-1)" )

gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "floxuridine", 5, "EGFR Copy Number" )

gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "moxifloxacin", 5, "EGFR Copy Number" )

gbm_candidate_prism_plot_cont(cell_gbm_info_longer_v2, "nimodipine", 5, "EGFR Copy Number" )

change the tmz resistant test compare the log2fold changes of the resistnat cells lines between drugs not within a drug.

cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$tmz_cell =="TMZ_RES", ]

tmz_v2 <- c()
for (i in 1:length(drugs)){
  cell_line_info<- cell_gbm_info_longer_v3[cell_gbm_info_longer_v3$drug %in% c(drugs[i], "temozolomide"),]
   #simvastain does have the A172 cell line so 
    # only keep cell lines with duplicated
  paired_cell_lines<- cell_line_info$displayName[duplicated(cell_line_info$displayName)]
  cell_line_info<- cell_line_info[cell_line_info$displayName %in% paired_cell_lines,]
  test<- as.vector(as.data.frame(cell_line_info[,17:18]))
  x<- test[test$drug  == drugs[i],2]
  y<- test[!test$drug ==  drugs[i],2]
  x<- as.numeric(unlist(x))
  y<- as.numeric(unlist(y))
   
    if(length(x) > 1 & length(y) > 1 ){
      res <- wilcox.test(x,y,alternative = "two.sided", paired = TRUE) 
       tmz_v2[i]<- res$p.value
    }else{
      print(i)
    }
    
}
## [1] 1
#replace the old tmz resistant p-values with the new p-values
colnames(test_res)
##  [1] "TP53 (LFS1, p53) Mutation 22Q2 Public"                                 
##  [2] "EGFR (ERBB1, ERBB) Mutation 22Q2 Public"                               
##  [3] "RB1 (PPP1R130, RB, OSRC) Mutation 22Q2 Public"                         
##  [4] "MTOR (FRAP1, FLJ44809, RAPT1, RAFT1, FRAP, FRAP2) Mutation 22Q2 Public"
##  [5] "PTEN (MHAM, PTEN1, MMAC1, BZS, TEP1) Mutation 22Q2 Public"             
##  [6] "sex"                                                                   
##  [7] "tmz"                                                                   
##  [8] "mgmt"                                                                  
##  [9] "egfr_copy_p"                                                           
## [10] "mgmt_methy_p"
tmz_v2[1]<- 1 # for temzolomide
test_res[,7]<- tmz_v2
#plot.data$stars <- cut(plot.data$p.value, breaks=c(-Inf, 0.001, 0.01, 0.05, Inf), label=c("***", "**", "*", "")) 
test_res_t <- t(test_res)
rownames(test_res_t)<- c("TP53 Mutation - Wilcox", "EGFR Mutation - Wilcox" , "RB1 Mutation - Wilcox", "MTOR Mutation - Wilcox", "PTEN Mutation - Wilcox", "Sex - Wilcox", "TMZ-Resistant - Wilcox Paired", "MGMT Promoter Methylation (high vs low) - Wilcox", "EGFR Copy Number - linear regression", "MGMT Promoter Methylation- linear regression"  )                                         
col_fun = colorRamp2(c(0,  1), c("blue", "white"))
Heatmap(test_res_t, nam= "p-value of biomarker and PRISM Drug Response", col = col_fun, column_names_rot = 45,
               clustering_distance_rows= "euclidean",
               clustering_distance_columns=  "euclidean",
               clustering_method_rows = "ward.D2" ,
               clustering_method_columns="ward.D2", 
        cell_fun = function(j, i, x, y, w, h, fill) {
        if(test_res_t[i, j] < 0.05) {
            grid.text("*", x, y)
        } else if(test_res_t[i, j] < 0.2) {
          grid.text("?", x, y)
      }})

top 5

cell_gbm_info_longer_v3<- cell_gbm_info_longer_v2[cell_gbm_info_longer_v2$drug %in% c(" pamidronate", "nimodipine", "moxifloxacin", "saxagliptin", "icosapent", "temozolomide"),]
p2 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, y= drug, color= drug))+ geom_point(stat = "identity") +theme_minimal() + xlab("PRISM Primary Screen log(fold change)") + ylab("GBM Cell Lines") + geom_vline(xintercept = 0.3) 

p1 <- ggplot(cell_gbm_info_longer_v3, aes(x=log2change, fill= drug)) + geom_density(alpha=.2) +theme_minimal() +  theme(
    axis.title.x = element_blank())+ geom_vline(xintercept = 0.3)

plot_grid(p1, p2, ncol = 1, align = "v")

Save Data

Save Figures

NA

END

Location of final scripts:
/script

Location of data produced:
na

Dates when operations were done:
220901

Versions

sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] ComplexHeatmap_2.10.0 circlize_0.4.15       viridis_0.6.2        
## [4] viridisLite_0.4.1     cowplot_1.1.1         ggplot2_3.3.6        
## [7] tidyr_1.2.1           readr_2.1.2          
## 
## loaded via a namespace (and not attached):
##  [1] png_0.1-7           assertthat_0.2.1    digest_0.6.29      
##  [4] foreach_1.5.2       utf8_1.2.2          R6_2.5.1           
##  [7] stats4_4.1.3        evaluate_0.16       highr_0.9          
## [10] pillar_1.8.1        GlobalOptions_0.1.2 rlang_1.0.6        
## [13] rstudioapi_0.13     jquerylib_0.1.4     S4Vectors_0.32.4   
## [16] GetoptLong_1.0.5    rmarkdown_2.16      labeling_0.4.2     
## [19] stringr_1.4.1       bit_4.0.4           munsell_0.5.0      
## [22] compiler_4.1.3      xfun_0.33           BiocGenerics_0.40.0
## [25] pkgconfig_2.0.3     shape_1.4.6         htmltools_0.5.3    
## [28] tidyselect_1.1.2    tibble_3.1.8        gridExtra_2.3      
## [31] IRanges_2.28.0      codetools_0.2-18    matrixStats_0.62.0 
## [34] fansi_1.0.3         crayon_1.5.2        dplyr_1.0.10       
## [37] tzdb_0.3.0          withr_2.5.0         jsonlite_1.8.0     
## [40] gtable_0.3.1        lifecycle_1.0.2     DBI_1.1.3          
## [43] magrittr_2.0.3      scales_1.2.1        cli_3.4.1          
## [46] stringi_1.7.8       vroom_1.5.7         cachem_1.0.6       
## [49] farver_2.1.1        doParallel_1.0.17   bslib_0.4.0        
## [52] ellipsis_0.3.2      generics_0.1.3      vctrs_0.4.2        
## [55] RColorBrewer_1.1-3  rjson_0.2.21        iterators_1.0.14   
## [58] tools_4.1.3         bit64_4.0.5         glue_1.6.2         
## [61] purrr_0.3.4         hms_1.1.2           parallel_4.1.3     
## [64] fastmap_1.1.0       yaml_2.3.5          clue_0.3-61        
## [67] colorspace_2.0-3    cluster_2.1.2       knitr_1.40         
## [70] sass_0.4.2